README.md

Conformalized Graph Neural Networks

This repository hosts the code base for the paper

Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Kexin Huang, Ying Jin, Emmanuel Candès, Jure Leskovec
NeurIPS 2023, Spotlight
Link to Paper

If you find this work useful, please consider cite:

@article{huang2023conformalized_gnn,
  title={Uncertainty quantification over graph with conformalized graph neural networks},
  author={Huang, Kexin and Jin, Ying and Candes, Emmanuel and Leskovec, Jure},
  journal={NeurIPS},
  year={2023}
}

Overview

Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncertainty estimates. Given an entity in the graph, CF-GNN produces a prediction set/interval that provably contains the true label with pre-defined coverage probability (e.g.~90%). We establish a permutation invariance condition that enables the validity of CP on graph data and provide an exact characterization of the test-time coverage. Besides valid coverage, it is crucial to reduce the prediction set size/interval length for practical use. We observe a key connection between non-conformity scores and network structures, which motivates us to develop a topology-aware output correction model that learns to update the prediction and produces more efficient prediction sets/intervals. Extensive experiments show that CF-GNN achieves any pre-defined target marginal coverage while significantly reducing the prediction set/interval size by up to 74% over the baselines. It also empirically achieves satisfactory conditional coverage over various raw and network features.

Installation

Install Torch and PyG following here and then do

pip install -r requirements.txt

Run

Datasets

Classification datasets are supported in PyG. For regression datasets, download from this link and put the folder under this repository.

Here are the list of datasets for classification tasks: Cora_ML_CF, DBLP_CF, CiteSeer_CF, PubMed_CF, Amazon-Computers, Amazon-Photo, Coauthor-CS, Coauthor-Physics

Here are the list of datasets for regression tasks: Anaheim, ChicagoSketch, county_education_2012, county_election_2016, county_income_2012, county_unemployment_2012, twitch_PTBR

Pre-trained GNN base models

To reproduce the paper result, please use the fixed pre-trained GNN base models from this link. This makes sure the gain is from the conformal adjustment instead of the noise in the base model training. After downloading and unzipping this link, please put the model folder under this repository.

If you wish to re-train GNN base model, simply remove the base model folder in this repository and the model will train again.

Key Arguments

• --model: base GNN model, select from 'GAT', 'GCN', 'GraphSAGE', 'SGC'
• --dataset: dataset name, select from 'Cora_ML_CF', 'CiteSeer_CF', 'DBLP_CF', 'PubMed_CF', 'Amazon-Computers', 'Amazon-Photo', 'Coauthor-CS', 'Coauthor-Physics', 'Anaheim', 'ChicagoSketch', 'county_education_2012', 'county_election_2016', 'county_income_2012', 'county_unemployment_2012', 'twitch_PTBR'
• --device: cuda device
• --alpha: pre-specified miscoverage rate, default is 0.1
• --optimal: use optimal hyperparameter set
• --hyperopt: conduct a sweep of hyperparameter optimization
• --num_runs: number of runs, default is 10
• --wandb: turn on weight and bias tracking
• --verbose: verbose mode, print out log (incl. training loss)
• --optimize_conformal_score: for classification only, options: aps and raps
• --not_save_res: default is saving the result to the pred folder, by adding this flag, you choose to NOT save the result
• --epochs: number of epochs for conformal correction

Training CF-GNN

python train.py --model GCN \
                --dataset Cora_ML_CF \
                --device cuda \
                --alpha 0.1\
                --optimal \
                --num_runs 1

Training baseline models

For classification datasets Cora_ML_CF, DBLP_CF, CiteSeer_CF, PubMed_CF, Amazon-Computers, Amazon-Photo, Coauthor-CS, Coauthor-Physics:

All baselines are calibration methods, choose calibrator from TS VS ETS CaGCN GATS.

python train.py --model GCN \
                --dataset Cora_ML_CF \
                --device cuda \
                --alpha 0.05 \
                --conf_correct_model Calibrate \
                --calibrator TS

For regression datasets Anaheim, ChicagoSketch, county_education_2012, county_election_2016, county_income_2012, county_unemployment_2012, twitch_PTBR:

To use mcDropout:

python train.py --model GCN \
                --dataset Anaheim \
                --device cuda \
                --alpha 0.05 \
                --conf_correct_model mcdropout_std

To use BayesianNN:

python train.py --model GCN \
                --dataset Anaheim \
                --device cuda \
                --alpha 0.05 \
                --bnn

To use QuantileRegression:

python train.py --model GCN \
                --dataset Anaheim \
                --device cuda \
                --alpha 0.05 \
                --conf_correct_model QR

Launching a hyper-parameter search for CF-GNN

python train.py --model GCN \
                --dataset Cora_ML_CF \
                --device cuda \
                --alpha 0.1 \          
                --hyperopt

Adjusting pre-specified coverage 1-alpha

This is the script for Fig 5(1).

for data in Anaheim Cora_ML_CF
do
for alpha in 0.05 0.1 0.15 0.2 0.25 0.3 
do
python train.py --model GCN --dataset $data --device cuda --optimal --alpha $alpha
done
done

Adjusting holdout calibration set fraction

This is the script for Fig 5(2).

for data in Anaheim Cora_ML_CF
do
for calib_frac in 0.1 0.3 0.7 0.9
do
python train.py --model GCN --dataset $data --device cuda --optimal --calib_fraction $calib_frac
done
done